SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D Scene Self-Reference-Cited by-同舟云学术

SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D Scene Self

Published:2021-12-04 Issue: Volume: Page:2100197
ISSN:2640-4567
Container-title:Advanced Intelligent Systems
language:en
Short-container-title:Advanced Intelligent Systems

Author:

Wang Guangming¹^ORCID,Jiang Chaokang²,Shen Zehang¹,Miao Yanzi²,Wang Hesheng¹^ORCID

Affiliation:

1. Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China

2. Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, School of Information and Control Engineering, Advanced Robotics Research Center China University of Mining and Technology Xuzhou 221116 China

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202100197

Reference43 articles.

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2. Sparse scene flow segmentation for moving object detection in urban environments

3. G.Wang X.Wu Z.Liu H.Wang inProc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition IEEE Piscataway NJ2021 pp.15910–15919.

4. P.Wang W.Li Z.Gao Y.Zhang C.Tang P.Ogunbona inProc. of the IEEE Conf. on Computer Vision and Pattern Recognition IEEE Piscataway NJ2017 pp.595–604.

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